期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2011
卷号：2011
出版社：ACL Anthology
摘要：We consider a very simple, yet effective, approach
to cross language adaptation of dependency
parsers. We first remove lexical items
from the treebanks and map part-of-speech
tags into a common tagset. We then train a
languagemodel on tag sequences in otherwise
unlabeled target data and rank labeled source
data by perplexity per word of tag sequences
from less similar to most similar to the target.
We then train our target language parser on
the most similar data points in the source labeled
data. The strategy achieves much better
results than a non-adapted baseline and stateof-
the-art unsupervised dependency parsing,
and results are comparable to more complex
projection-based cross language adaptation algorithms.
